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Dual graph convolutional neural network for predicting chemical networks

BMC BIOINFORMATICS 2020年第3-Sup期21卷 94-94页

作者： Harada, Shonosuke Akita, Hirotaka Tsubaki, Masashi Baba, Yukino Takigawa, Ichigaku Yamanishi, Yoshihiro Kashima, Hisashi Kyoto Univ Kyoto 6068501 Japan Preferred Networks Tokyo 1000004 Japan Natl Inst Adv Ind Sci & Technol Tokyo 1350064 Japan Tsukuba Univ Tsukuba Ibaraki 3058577 Japan Hokkaido Univ Sapporo Hokkaido 0600808 Japan Kyushu Inst Technol Fukuoka 8208502 Japan Riken AIP Tokyo 1030027 Japan

Background Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of available data has enabled applications of computational approaches such as statistical modeling and machine learning method. Both a set of chemical interactions and chemical compound structures are represented as graphs, and various graph-based approaches including graph convolutional neural networks have been successfully applied to chemical network prediction. However, there was no efficient method that can consider the two different types of graphs in an end-to-end manner. Results We give a new formulation of the chemical network prediction problem as a link prediction problem in a graph of graphs (GoG) which can represent the hierarchical structure consisting of compound graphs and an inter-compound graph. We propose a new graph convolutional neural network architecture called dual graph convolutional network that learns compound representations from both the compound graphs and the inter-compound network in an end-to-end manner. Conclusions Experiments using four chemical networks with different sparsity levels and degree distributions shows that our dual graph convolution approach achieves high prediction performance in relatively dense networks, while the performance becomes inferior on extremely-sparse networks.

关键词： Chemical network prediction graph convolutional neural network graph of graphs

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Gesture recognition of graph convolutional neural network based on spatial domain

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neural COMPUTING & APPLICATIONS 2023年第3期35卷 2157-2167页

作者： Chen, Hong Zhao, Hongdong Qi, Baoqiang Zhang, Shuai Yu, Zhanghong Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300130 Peoples R China Hebei Normal Univ Sci & Technol Sch Math & Informat Technol Qinhuangdao 066004 Hebei Peoples R China Qinhuangdao Inst Technol Dept Informat Engn Qinhuangdao 066100 Hebei Peoples R China

With the iterative update of computer technology, the penetration of computer and other Internet technologies in human-computer interaction systems has become more and more extensive, and the human-computer interaction methods have quietly undergone huge changes. Gesture recognition has gradually become a hot spot in the field of human-computer interaction now, which has a wide range of application prospects and research value. The color segmentation experiment shows that the skin color of the gesture in the YCrCg space has better clustering properties than in the YCrCb space. In the preprocessing of gesture images, an improved Otsu method is proposed to improve the real-time performance to realize the threshold segmentation of the human hand;then the morphological processing is carried out, and the median filter method is used to achieve image denoising to improve image quality. A gesture recognition algorithm is designed: First, use graph-SAGE to recognize the graph-structured data of the gesture, and then use the Adaboost algorithm to combine the two strong classifiers of the random forest and the support vector machine into a cascade classifier through the cascade structure. The output information of graph-SAGE is classified and the meaning of the gesture is analyzed. On the test set, the average detection accuracy of the algorithm is 91.70%, the recall rate is 94.23%, and the average detection time per frame is 330 ms.

关键词： graph convolutional neural network Gesture recognition graph-SAGE model Cascade classifier

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EEG-Based Parkinson's Disease Recognition via Attention-Based Sparse graph convolutional neural network

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第11期27卷 5216-5224页

作者： Chang, Hongli Liu, Bo Zong, Yuan Lu, Cheng Wang, Xuenan Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Key Lab Child Dev & Learning Sci Minist Educ Nanjing 210096 Peoples R China Southeast Univ Sch Biol Sci & Med Engn Nanjing 210096 Peoples R China Shandong First Med Univ & Shandong Acad Med Sci Shandong Inst brain Sci & Brain Inspired Res Jinan 250117 Peoples R China Shandong First Med Univ & Shandong Acad Med Sci Sch Radiol Tai An 271016 Peoples R China

Parkinson's disease (PD) is a complicated neurological ailment that affects both the physical and mental wellness of elderly individuals which makes it problematic to diagnose in its initial stages. Electroencephalogram (EEG) promises to be an efficient and cost-effective method for promptly detecting cognitive impairment in PD. Nevertheless, prevailing diagnostic practices utilizing EEG features have failed to examine the functional connectivity among EEG channels and the response of associated brain areas causing an unsatisfactory level of precision. Here, we construct an attention-based sparse graph convolutional neural network (ASGCNN) for diagnosing PD. Our ASGCNN model uses a graph structure to represent channel relationships, the attention mechanism for selecting channels, and the L1 norm to capture channel sparsity. We conduct extensive experiments on the publicly available PD auditory oddball dataset, which consists of 24 PD patients (under ON/OFF drug status) and 24 matched controls, to validate the effectiveness of our method. Our results show that the proposed method provides better results compared to the publicly available baselines. The achieved scores for Recall, Precision, F1-score, Accuracy and Kappa measures are 90.36%, 88.43%, 88.41%, 87.67%, and 75.24%, respectively. Our study reveals that the frontal and temporal lobes show significant differences between PD patients and healthy individuals. In addition, EEG features extracted by ASGCNN demonstrate significant asymmetry in the frontal lobe among PD patients. These findings can offer a basis for the establishment of a clinical system for intelligent diagnosis of PD by using auditory cognitive impairment features.

关键词： Parkinson's disease (PD) electroence-phalogram (EEG) graph convolutional neural network channel selection asymmetry

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Exploring the Internet of Things sequence-structure detection and supertask network generation of temporal-spatial-based graph convolutional neural network

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JOURNAL OF SUPERCOMPUTING 2022年第4期78卷 5029-5049页

作者： Liu, Xiao Qi, De-yu Li, Wen-lin Zhang, Hao-tong South China Univ Technol Coll Comp Sci & Technol Guangzhou 510006 Peoples R China

The study is designed to improve the efficiency of Internet of Things (IoT) structure detection and achieve the smooth operation of IoT networks. First, the connection between the IoT network structure and maxdegree is investigated based on analyzing the IoT supertask network structure to find the main influence factor of maxdegree, along with the conditions for obtaining the optimal maxdegree. Second, a structural algorithm model of optimal supertask network is proposed as the foundation for achieving the minimum maxdegree. Finally, the human behavior recognition database is taken as the research object to verify its performance through the specific instance data. The IoT network structure factors are proved to include the task quantity, resource capacity, number of networks, and Communication Calculation Ratio (CCR). The experimental results also show that the principal factor that affects maxdegree is the number of different tasks. Besides, there is a mutually positive interaction between the network structure and the IoT maxdegree, which complement each other and form the core network of IoT. Moreover, the results reflect the good performance on different datasets of the supertask IoT network structure for the human behavior recognition database. There exists the optimal maxdegree of the model under the condition of 40 tasks, 32 resources, 6 networks and CCR of 6. Furthermore, the proposed algorithm has a shorter length and lower complexity than other related algorithms, which is very suitable for the construction of IoT networks. The research results can provide some references and practical value for the construction and data processing of the IoT structure.

关键词： graph convolutional neural network Internet of Things Sequence-structure detection Supertask gateway Maxdegree

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Rapid optimization for inner thermal layout in horizontal annuli using genetic algorithm coupled graph convolutional neural network

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 150卷

作者： Feng, Feng Li, Yu-Bai Chen, Zhi-Hua Wu, Wei-Tao Peng, Jiang-Zhou Mei, Mei Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116023 Peoples R China Nanjing Univ Sci & Technol Key Lab Transient Phys Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sino French Engineer Sch Nanjing 210094 Peoples R China

The present study introduces a novel optimization framework that combines a graph convolutional neural network surrogate model with Genetic Algorithms (GCN-GA). This framework can optimize the heat sources layout in horizontal annuli, achieving the highest heat transfer efficiency. The predictive GCN surrogate model utilizes graph data as training input and enables it with sensitivity to geometric variations at the node level. This sensitivity allows achieving higher training and predictive accuracy compared to fully connected or convolu-tional neural networks. The optimization is achieved by GA with global search capabilities seeking the optimal spatial heat sources layout. The predicted average Nusselt numbers of the horizontal annuli by GCN are utilized as evaluation metrics. Simultaneously, geometric parameter features are targeted as optimization objectives, achieving optimization results consistent with numerical results across various cases. Furthermore, the GCN predicts temperature fields with average errors below 1.33% compared to numerical results. Specifically, in the cases validation involving single, double, and triple heat sources, the GCN-GA demonstrates faster optimization speeds, increased by 10, 29, and 110 times compared to numerical methods, respectively. Therefore, significant enhancements in optimization efficiency facilitated by the optimization framework built upon the GCN surrogate model.

关键词： Natural convection Optimization graph convolutional neural network Genetic algorithm

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SMGCN: Multiple Similarity and Multiple Kernel Fusion Based graph convolutional neural network for Drug-Target Interactions Prediction

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第1期21卷 143-154页

作者： Wang, Wei Yu, Mengxue Sun, Bin Li, Juntao Liu, Dong Zhang, Hongjun Wang, Xianfang Zhou, Yun Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Peoples R China Key Lab Artificial Intelligence & Personalized Lea Xinxiang 453007 Peoples R China Henan Normal Univ Sch Math & Informat Sci Xinxiang 453007 Peoples R China Henan Polytech Univ Hebi Inst Engn & Technol Hebi 458030 Peoples R China Henan Inst Technol Coll Comp Sci & Technol Xinxiang 453000 Henan Peoples R China

Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated process. Therefore, we propose a novel method called SMGCN, which combines multiple similarity and multiple kernel fusion based on graph convolutional network (GCN) to predict DTIs. In order to capture the features of the network structure and fully explore direct or indirect relationships between nodes, we propose the method of multiple similarity, which combines similarity fusion matrices with Random Walk with Restart (RWR) and cosine similarity. Then, we use GCN to extract multi-layer low-dimensional embedding features. Unlike traditional GCN methods, we incorporate Multiple Kernel Learning (MKL). Finally, we use the Dual Laplace Regularized Least Squares method to predict novel DTIs through combinatorial kernels in drug and target spaces. We conduct experiments on a golden standard dataset, and demonstrate the effectiveness of our proposed model in predicting DTIs through showing significant improvements in Area Under the Curve (AUC) and Area Under the Precision-Recall Curve (AUPR). In addition, our model can also discover some new DTIs, which can be verified by the KEGG BRITE Database and relevant literature.

关键词： Drugs Kernel Proteins Predictive models Feature extraction convolutional neural networks Compounds Drug discovery drug-target interactions graph convolutional neural network multiple similarity

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Intelligent graph convolutional neural network for Road Crack Detection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第8期24卷 8475-8482页

作者： Djenouri, Youcef Belhadi, Asma Houssein, Essam H. Srivastava, Gautam Lin, Jerry Chun-Wei SINTEF Digital N-0314 Oslo Norway Kristiania Univ Coll N-0153 Oslo Norway Minia Univ Fac Comp & Informat Al Minya 61519 Egypt Brandon Univ Dept Math & Comp Sci Brandon MB R7A 6A9 Canada China Med Univ Res Ctr Interneural Comp Taichung 40402 Taiwan Lebanese Amer Univ Dept Comp Sci & Math Beirut 1102 Lebanon Western Norway Univ Appl Sci Dept Comp Sci Elect Engn & Math Sci N-5063 Bergen Norway

This paper presents a novel intelligent system based on graph convolutional neural networks to study road crack detection in intelligent transportation systems. The visual features of the input images are first computed using the well-known Scale-Invariant Feature Transform (SIFT) extraction algorithm. Then, a correlation between SIFT features of similar images is analyzed and a series of graphs are generated. The graphs are trained on a graph convolutional neural network, and a hyper-optimization algorithm is developed to supervise the training process. A case study of road crack detection data is analyzed. The results show a clear superiority of the proposed framework over state-of-the-art solutions. In fact, the precision of the proposed solution exceeds 70%, while the precision of the baseline methods does not exceed 60%.

关键词： Roads Feature extraction convolutional neural networks Anomaly detection Visualization Training Behavioral sciences graph convolutional neural network road crack detection intelligent transportation systems SIFT extractor

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GNet4FL: effective fault localization via graph convolutional neural network

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AUTOMATED SOFTWARE ENGINEERING 2023年第2期30卷 1-26页

作者： Qian, Jie Ju, Xiaolin Chen, Xiang Nantong Univ Sch Informat Sci & Technol Nantong 226019 Jiangsu Peoples R China

Fault localization aims to efficiently locate faults when debugging programs, reducing software development and maintenance costs. Spectrum-based fault location (SBFL) is the most commonly used fault location technology, which calculates and ranks the suspicious value of each program entity with a specific formula by counting the coverage information of all the program entities and execution results of test cases. However, previous SBFL techniques suffered from low accuracy due to the sole use of execution coverage. This paper proposed an approach GNet4FL based on the graph convolutional neural network. GNet4FL first collects static features based on code structure and dynamic features based on test results. Then, GNet4FL uses graphSAGE to obtain node representation of source codes and performs feature fusion on an entity consisting of multiple nodes, which preserves the topological information of the graph. Finally, the representation of each entity is input to the multi-layer perceptron for training and ranking entities. The results of the study showed that GNet4FL successfully located 160 out of 262 faults, outperforming the three state-of-the-art methods by 94, 42, and 14% in Top-1 accuracy, and having close results to Grace with less cost. Furthermore, we investigated the impact of each component (i.e., graph neural network, pruning, and dynamic features) of GNet4FL on the results. We found that all of these components had a positive impact on the proposed approach.

关键词： Fault localization graph convolutional neural network Abstract syntax tree

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The Digital Reform of Japanese Classroom Teaching Modes Under the graph convolutional neural network

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IEEE ACCESS 2024年 12卷 95118-95127页

作者： Jiang, Gongyi Gao, Weijun Tong, Mingjia Tourism Coll Zhejiang Foreign Languages Dept Hangzhou 310043 Peoples R China Univ Kitakyushu Fac Environm Engn Kitakyushu 8080135 Japan

This work aims to explore reform methods for Japanese classroom teaching modes in an informationized environment. Addressing issues such as monotonous classroom content and low student engagement in traditional teaching, this work introduces deep learning technology and constructs a personalized recommendation model based on the Bidirectional Encoder Representations from Transformer (BERT) fused with the graph convolutional neural network (GCNN). Furthermore, this model is applied to Japanese flipped classroom teaching, and a personalized recommendation system-supported Japanese flipped classroom mode is designed. The performance of the constructed model is finally verified. The results show that the personalized recommendation model constructed here has achieved good application effects in Japanese teaching, with a recommendation recognition accuracy of 95.96% and an F1 value of 90.12%. The constructed model exceeds the precision of the baseline algorithm convolutional neural network (CNN) by over 4%. Through questionnaire surveys and empirical analysis of controlled experimental designs, it is found that the Japanese flipped classroom mode supported by the personalized recommendation system demonstrates significant advantages in enhancing student learning abilities and satisfaction. The improvement rates of experimental group students in various indicators all exceed 18%. Therefore, the Japanese teaching mode proposed can significantly improve the teaching effectiveness of Japanese classrooms and provide experimental references for educational mode reforms.

关键词： Education Vectors convolutional neural networks Electronic learning Online services Encoding Recommender systems Deep learning Learning systems graph convolutional networks Teaching modes deep learning personalized recommendation Japanese classroom flipped classroom graph convolutional neural network

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graph convolutional neural network with Multi-Scale Attention Mechanism for EEG-Based Motion Imagery Classification

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第14期37卷 2354020-2354020页

作者： Zhu, Jun Liu, Qingshan Xu, Chentao Southeast Univ Sch Math Nanjing 210096 Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China

Recently, deep learning has been widely used in the classification of EEG signals and achieved satisfactory results. However, the correlation between EEG electrodes is rarely considered, which has been proved that there are indeed connections between different brain regions. After considering the connections between EEG electrodes, the graph convolutional neural network is applied to detect human motor intents from EEG signals, where EEG data are transformed into graph data through phase lag index, time-domain and frequency-domain features with different signal bands. Meanwhile, a multi-scale attention mechanism is proposed to the network to improve the accuracy of classification. By using the multi-scale attention-based graph convolutional neural network, the accuracy of 93.22% is achieved with 10-fold cross-validation, which is higher than the compared methods which ignore the spatial correlations of EEG signals.

关键词： graph convolutional neural network multi-scale attention mechanism EEG classification

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